Production of synthetic fuels by high‐temperature co‐electrolysis of carbon dioxide and steam with Fischer‐Tropsch synthesis
Bibliographic record
Abstract
Numerous entities are currently involved in the production of synthetic fuels using various feedstock options including natural gas, refuse derived fuel, landfill gas, anaerobic digester gas, coal, and mixtures of these inputs. Current world‐class FT plants (∼15 900 m3/d or 100 000 barrels per day) require large deposits of natural gas (i.e. ∼1.78 m3/L/d or 10 MSCF/BPD means gas sources that can provide ∼10 billion m3 per year for plant lifetime). Modular Fischer‐Tropsch (FT) reactors currently under development have some unique features that reduce cost and provide the ability to utilize sources of natural gas, biomass, or other under‐utilized sources of energy that would otherwise not be developed. The current Ceramatec modular FT reactor operates with a fixed bed size of 10 cm diameter using an internal heat transfer structure to keep axial and radial temperature variation to < 10 °C. The internal heat transfer media avoids the traditional problem with fixed bed reactors of heat removal from highly exothermic reactions. These reactors can also be used to mitigate the current concern with emissions of carbon dioxide when used in conjunction with high‐temperature co‐electrolysis (HTCE) of carbon dioxide and water to produce synthesis gas and subsequently synthetic fuels. When combined with a non‐carbon source of electricity such as wind, solar, biomass gasification, or anaerobic digester gas, it is possible to store intermittent sources of electricity as liquid fuels for later use. The HTCE generally operates at a voltage of 1.32 V and a current density of ∼300 mA/cm2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".